class_docstr: Quantized counterpart of the nn.Linear module that applies QuantNoise during training.
  
  Args:
      - in_features: input features
      - out_features: output features
      - bias: bias or not
      - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights)
      - bits: number of bits
      - method: choose among {"tensor", "histogram", "channel"}
      - update_step: recompute scale and zero_point every update_steps iterations
  
  Remarks:
      - We use the straight-through estimator so that the gradients
        back-propagate nicely in the network, this is implemented with
        the detach() trick.
      - Parameters scale and zero_point are recomputed every update_step
        forward pass to reduce the overhead
      - At test time, the weights are fully quantized
code: def reset_parameters(self):
      nn.init.xavier_uniform_(self.weight)
      if self.chosen_bias:
          nn.init.constant_(self.bias, 0.0)
      return
  
file_path: fairseq/fairseq/modules/quantization/scalar/modules/qlinear.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ..ops import emulate_int
label_desc: Use image node_id 2 for calling the IntLinear obj's underlying member method code with example usage: obj.reset_parameters() without return types